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Prognostic Value of Glucose-to-Lymphocyte Ratio in Critically Ill Patients with Acute Pancreatitis
BACKGROUND: Glucose metabolism and systemic inflammation have been associated with prognosis in acute pancreatitis (AP) patients. However, the possible value as a prognostic marker of the glucose-to-lymphocyte ratio (GLR) has not been evaluated in critically ill patients with AP. METHODS: This study...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8436258/ https://www.ncbi.nlm.nih.gov/pubmed/34526812 http://dx.doi.org/10.2147/IJGM.S327123 |
Sumario: | BACKGROUND: Glucose metabolism and systemic inflammation have been associated with prognosis in acute pancreatitis (AP) patients. However, the possible value as a prognostic marker of the glucose-to-lymphocyte ratio (GLR) has not been evaluated in critically ill patients with AP. METHODS: This study included 1,133 critically ill patients with AP from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database, who were randomly divided into the training cohort (n=806) and the validation cohort (n=327) at a ratio of 7:3. X-tile software was used to determine the optimal cut-off values for GLR. Area under the curve (AUC) analysis was performed to compare the performance between GLR and other blood-based inflammatory biomarkers. Univariate and multivariate Cox regression analyses were applied to select prognostic factors associated with in-hospital mortality. A nomogram model was developed based on the identified prognostic factors and the validation cohort was used to further validate the nomogram. RESULTS: The optimal cut-off value for GLR was 0.9. The ROC analyses showed that the discrimination abilities of GLR were better than other blood-based inflammatory biomarkers. Multivariate Cox regression analysis demonstrated that age, platelet, albumin, bilirubin, Sequential Organ Failure Assessment (SOFA) score, and GLR are independent predictors of poor overall survival in the training cohort and were incorporated into the nomogram for in-hospital mortality as independent factors. The nomogram exhibited better discrimination with C-indexes in the training cohort and the validation cohort of 0.886 (95% CI=0.849–0.922) and 0.841 (95% CI=0.767–0.915), respectively. The calibration plot revealed an adequate fit of the nomogram for predicting the risk of in-hospital mortality in both sets. CONCLUSION: As an easily available biomarker, GLR can independently predict the in-hospital mortality of critically ill patients with AP. The nomogram combining GLR with other significant features exerted favorable predictive performance for in-hospital mortality. |
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